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- W4226340010 abstract "Normalizing flows have grown more popular over the last few years; however, they continue to be computationally expensive, making them difficult to be accepted into the broader machine learning community. In this paper, we introduce a simple one-dimensional one-layer network that has closed form Lipschitz constants; using this, we introduce a new Exact-Lipschitz Flow (ELF) that combines the ease of sampling from residual flows with the strong performance of autoregressive flows. Further, we show that ELF is provably a universal density approximator, more computationally and parameter efficient compared to a multitude of other flows, and achieves state-of-the-art performance on multiple large-scale datasets." @default.
- W4226340010 created "2022-05-05" @default.
- W4226340010 creator A5087598801 @default.
- W4226340010 date "2021-12-13" @default.
- W4226340010 modified "2023-09-24" @default.
- W4226340010 title "ELF: Exact-Lipschitz Based Universal Density Approximator Flow" @default.
- W4226340010 hasPublicationYear "2021" @default.
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